Half the spend. Half the load. Zero LLM tokens.
Tarragon sits in the data path between your agents and the model, removing the waste before the model runs. Pure ML. No second LLM, no rewriting, no extra inference cost.
Most of what your agents send the model is waste.
Inference costs running past budget, and agents getting less reliable as they scale. Same root cause for both. Agents push far more data into the model than it uses, and you pay for all of it while the noise degrades the output.
Jira returns 8,000 tokens.
The model uses 340.
The other 95.7% is paid waste, and it ships on every call.
Every agent that goes to production will hit this wall.
More data
Agents pull from more tools, sources, and APIs. Each returns more than the model needs.
Deeper sessions
Multi-step workflows run 10-25 turns. Context grows every step. Redundancy compounds.
More agents
One team runs 5 agents today, 50 next year. The cost scales with the fleet, not the headcount.
Five stages. One loop.
Connect in minutes. See what no tool shows. Know what to fix first. Prove it's safe. Fix it.
Stacked, measured, guaranteed.
Runs inside your environment.
Deploys inside your VPC or on-prem, runs purely on your infrastructure, and never sends data to a third party. No second LLM in the path, no tokens consumed to save tokens.